A Question/Answer (QA) system answers questions posed in a natural language format by applying advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies. QA systems differ from typical document search technologies because document search technologies return a list of documents ranked in order of relevance to a word query, whereas QA systems receives a question expressed in a natural language, seeks to understand the question in much greater detail, and returns a precise answer to the question.
System developers may train QA systems to specific domains to provide more relevant answers to domain-specific questions (e.g., financial domain, travel domain, etc.). Training a QA system for a new domain, however, is time consuming. One approach to training a QA system is for a set of experts in a field to input detailed domain training knowledge into the QA system. Another approach to training a QA system is to capture corpora from trusted, traditional sources (textbooks, journals) that include accurate information. These traditional sources, however, have time-dated information due to their publication frequency. For example, most publishers publish textbooks every few years and publish journals on a monthly or quarterly basis. As such, QA systems trained from traditional sources may not return an up-to-date answer for a given question.